{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAoCISEH9P42ClbqXymhAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25acce915c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "rng = np.random.RandomState(1)\n",
    "# 指定形状的矩阵\n",
    "# rng.rand(2, 2)\n",
    "# 2行2列乘2行200列的矩阵\n",
    "X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T\n",
    "# print(X.shape)\n",
    "# print(X)\n",
    "# print(X[:3, :].shape)\n",
    "plt.scatter(X[:, 0], X[:, 1])\n",
    "plt.axis('equal');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
